Cameron Voisey is a machine learning engineer with eight years of experience bridging data science, software engineering, and particle physics. A former PhD physicist from Cambridge and UCL, he has applied rigorous research techniques to practical ML and data problems—ranging from LHC proton-structure frameworks to telecom access-network platforms. He’s contributed backend and DevOps improvements to notable open-source projects like magma, focusing on robustness, error handling, and maintainability. Having worked across consulting, healthcare data science, and industry ML roles, he brings a pragmatic, production-first approach to model and system design. Based in Cambridge with Norwegian ties, he pairs deep quantitative training with hands-on engineering instincts and a knack for making complex systems reliable under real-world constraints.
8 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD Physics, Doctor of Philosophy - PhD Physics at University of Cambridge
Platform for building access networks and modular network services
Role in this project:
Backend Developer & DevOps Engineer
Contributions:401 reviews, 70 commits, 233 PRs in 9 months
Contributions summary:Cameron's contributions focused on refining the platform's error handling and system integration. This included excluding specific errors from monitoring, enabling Redis integration in Sentry, and updating error code imports. Furthermore, the user worked on cleaning up unused variables and imports, indicating efforts towards code maintainability. They also contributed to formatting various files using clang-format-11 and modifying integration tests.
Platform for building access networks and modular network services
Contributions:45 PRs, 441 pushes, 143 branches in 1 year 1 month
networknetworkingmodular
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Cameron Voisey - Machine Learning Engineer at Storebrand